Ricardo Baeza-Yates

AIJ Journal 2025 Journal Article

Human-AI coevolution

• Dino Pedreschi
• Luca Pappalardo
• Emanuele Ferragina
• Ricardo Baeza-Yates
• Albert-László Barabási
• Frank Dignum
• Virginia Dignum
• Tina Eliassi-Rad

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., scientific, legal and socio-political.

IJCAI Conference 2025 Conference Paper

Human-AI Coevolution (Abstract Reprint)

• Dino Pedreschi
• Luca Pappalardo
• Emanuele Ferragina
• Ricardo Baeza-Yates
• Albert-László Barabási
• Frank Dignum
• Virginia Dignum
• Tina Eliassi-Rad

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices through online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often “unintended” systemic outcomes. This paper introduces human-AI coevolution as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i. e. , scientific, legal and socio-political.

IS Journal 2024 Journal Article

Responsible AI: An Urgent Mandate

• Ricardo Baeza-Yates
• Usama M. Fayyad

AI is rapidly becoming essential in various industries, raising societal expectations. AI’s societal consequences include impacts on mental health; misinformation; workforce displacement; and economic, regulatory, and law enforcement challenges. Indeed, the regulation of AI usage is on the horizon, with the European Union and China already taking big steps, while the United States drafted its first AI-related bill of rights last year. Professional associations and other nonprofits are also contributing to AI ethics and regulations, increasing the urgency and criticality of this area. In this new context, public services and regulated institutions must ensure responsible AI to avoid biased or inaccurate decision-making. Similarly, companies using AI responsibly can stand out, increase efficiency, and avoid future legal problems. This article highlights the issues and problems that result in many organizations not knowing how to do responsible AI in practice, as they need to identify potential problems, set up safeguards, and conduct ethical impact assessments, among other actions. We present the issues to consider toward a comprehensive approach to responsible AI that should include defining a responsible AI strategy road map; assessing models, processes, and products; and training individuals at different levels. By covering the pressing issues related to the urgent need for adopting responsible AI, we hope to highlight the importance for corporations to seriously consider responsible AI as they rush to adopt this technology for competitive advantage.

TCS Journal 2009 Journal Article

On the size of Boyer–Moore automata

• Ricardo Baeza-Yates
• Véronique Bruyère
• Olivier Delgrange
• Rodrigo Scheihing

In this work we study the size of Boyer–Moore automata introduced in Knuth, Morris & Pratt’s famous paper on pattern matching. We experimentally show that a finite class of binary patterns produce very large Boyer–Moore automata, and find one particular case which we conjecture, generates automata of size Ω ( m 6 ). Further experimental results suggest that the maximal size could be a polynomial of O ( m 7 ), or even an exponential O ( 2 0. 4 m ), where m is the length of the pattern.

IS Journal 2008 Journal Article

Near-Term Prospects for Semantic Technologies

• V. Richard Benjamins
• John Davies
• Ricardo Baeza-Yates
• PETER MIKA
• Hugo Zaragoza
• Mark Greaves
• Jose Manuel Gomez-Perez
• Jesus Contreras

Tor the past few years, the Semantic Web has been enjoying significant investment, mostly through research but to a lesser extent through start-ups and commercial projects. A major topic of discussion is where we can see those investments' results, so I asked several experts to consider what semantic technology will accomplish in the near future. The experts are from academia, venture capitalist firms, and companies focused on semantic technology, telecommunication, and Web 2. 0.

TCS Journal 2003 Journal Article

Optimal binary search trees with costs depending on the access paths

• Jayme L. Szwarcfiter
• Gonzalo Navarro
• Ricardo Baeza-Yates
• Joísa de S. Oliveira
• Walter Cunto
• Nívio Ziviani

We describe algorithms for constructing optimal binary search trees, in which the access cost of a key depends on the k preceding keys which were reached in the path to it. This problem has applications to searching on secondary memory and robotics. Two kinds of optimal trees are considered, namely optimal worst case trees and weighted average case trees. The time and space complexities of both algorithms are O(n k+2) and O(n k+1), respectively. The algorithms are based on a convenient decomposition and characterizations of sequences of keys which are paths of special kinds in binary search trees. Finally, using generating functions, we present an exact analysis of the number of steps performed by the algorithms.

TCS Journal 1997 Journal Article

Preface

• Ricardo Baeza-Yates
• Eric Goles